1. Field of the Invention
The present invention relates to an application-driven method and apparatus for limiting the energy consumption associated with the operation of a processor-controlled hardware platform. More specifically, this invention pertains to an operating system having an architecture in which energy consumption is limited through the interaction of appropriately-configured system layers linked by application-program interface (“API”) calls.
2. Description of the Prior Art
Battery-operated portable systems, especially those intended for commercial applications such as palmtop computers and PDA's, typically require low cost coupled with fast time to market. Such systems impose tight constraints on energy consumption. Battery capacity has improved slowly (a factor of 2 to 4 over the past 30 years) compared to the drastic increase in computational demands over this same period of time.
Typical portable appliances include a microprocessor-based computer architecture that, coupled with embedded operating systems, simplifies application development. The energy efficiency of such systems can typically be improved by design that is aware of not only performance, but also energy consumption of both hardware and software coupled with intelligent utilization of system components at run-time.
The operation system (“OS”) is situated to play a major role in the coordination of system power management for processor-based systems such as palmtop computers and PDA's. “Power Management Policy” refers to a set of actions taken by the OS to configure the processor and peripherals for the purpose of reducing system power consumption. Typical actions of an OS in this regard include (1) tuning processor voltage and frequency (2) switching among the different power states available to both the processor and other system devices.
While a few scheduling and resource allocation algorithms aimed at reducing the system power consumption have been attempted in operating systems running on hardware platforms, such techniques have heavily relied upon mathematical modeling techniques of a predictive nature to generate power management policies that employ timed turn-offs and the like. Such techniques are often statistically-based and blind to real time variations that can result in drastically ineffective operational power management policies.